{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = \"\"\"\n",
    "Fork of sever-ensemble-3(version 7/7)\n",
    "9 hours ago by Karl Hornlund\n",
    "\n",
    "From Kernel [Fork of sever-ensemble-3]\n",
    "Succeeded\n",
    "0.90370\n",
    "0.91840\n",
    "Fork of sever-ensemble-3(version 6/7)\n",
    "10 hours ago by Karl Hornlund\n",
    "\n",
    "From Kernel [Fork of sever-ensemble-3]\n",
    "Succeeded\n",
    "0.90274\n",
    "0.91844\n",
    "sever-ensemble-short(version 2/3)\n",
    "10 hours ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-ensemble-short]\n",
    "Succeeded\n",
    "0.89713\n",
    "0.91829\n",
    "Fork of sever-ensemble-3(version 4/7)\n",
    "19 hours ago by Karl Hornlund\n",
    "\n",
    "From Kernel [Fork of sever-ensemble-3]\n",
    "Succeeded\n",
    "0.88639\n",
    "0.91794\n",
    "Fork of sever-ensemble-3(version 1/7)\n",
    "20 hours ago by Karl Hornlund\n",
    "\n",
    "From Kernel [Fork of sever-ensemble-3]\n",
    "Succeeded\n",
    "0.90259\n",
    "0.91854\n",
    "sever-ensemble-3-clahe(version 2/2)\n",
    "a day ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-ensemble-3-clahe]\n",
    "Succeeded\n",
    "0.90876\n",
    "0.91809\n",
    "sever-submission-f32-gpu(version 10/10)\n",
    "a day ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-submission-f32-gpu]\n",
    "Succeeded\n",
    "0.89496\n",
    "0.91563\n",
    "sever-submission-f32-gpu(version 9/10)\n",
    "2 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-submission-f32-gpu]\n",
    "Succeeded\n",
    "0.89121\n",
    "0.91582\n",
    "sever-ensemble-3(version 11/11)\n",
    "2 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-ensemble-3]\n",
    "Succeeded\n",
    "0.90951\n",
    "0.91812\n",
    "sever-ensemble-3(version 8/11)\n",
    "2 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-ensemble-3]\n",
    "Succeeded\n",
    "0.90895\n",
    "0.91798\n",
    "sever-submission-f32-gpu(version 7/10)\n",
    "2 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-submission-f32-gpu]\n",
    "Succeeded\n",
    "0.88045\n",
    "0.89712\n",
    "sever-submission-f32-gpu(version 5/10)\n",
    "2 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-submission-f32-gpu]\n",
    "Succeeded\n",
    "0.90038\n",
    "0.91744\n",
    "sever-submission-f32-gpu(version 3/10)\n",
    "2 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-submission-f32-gpu]\n",
    "Succeeded\n",
    "0.89769\n",
    "0.91665\n",
    "sever-submission-f32-gpu(version 2/10)\n",
    "3 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-submission-f32-gpu]\n",
    "Succeeded\n",
    "0.89647\n",
    "0.91685\n",
    "sever-submission-f32-gpu(version 1/10)\n",
    "3 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-submission-f32-gpu]\n",
    "Succeeded\n",
    "0.89114\n",
    "0.91727\n",
    "sever-submission-f32-3c(version 2/2)\n",
    "3 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-submission-f32-3c]\n",
    "Succeeded\n",
    "0.89900\n",
    "0.91475\n",
    "sever-ensemble-3(version 7/11)\n",
    "4 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-ensemble-3]\n",
    "Succeeded\n",
    "0.90897\n",
    "0.91796\n",
    "sever-submission-f32(version 49/50)\n",
    "4 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-submission-f32]\n",
    "Succeeded\n",
    "0.89947\n",
    "0.91634\n",
    "sever-submission-f32(version 46/50)\n",
    "4 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-submission-f32]\n",
    "Succeeded\n",
    "0.89458\n",
    "0.91426\n",
    "sever-ensemble-3(version 5/11)\n",
    "4 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-ensemble-3]\n",
    "Succeeded\n",
    "0.90696\n",
    "0.91792\n",
    "sever-submission-f32(version 45/50)\n",
    "5 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-submission-f32]\n",
    "Succeeded\n",
    "0.88505\n",
    "0.91234\n",
    "sever-submission-f32(version 43/50)\n",
    "5 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-submission-f32]\n",
    "Succeeded\n",
    "0.89951\n",
    "0.91595\n",
    "sever-ensemble-classification(version 4/4)\n",
    "5 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-ensemble-classification]\n",
    "Succeeded\n",
    "0.90952\n",
    "0.91782\n",
    "sever-ensemble-classification(version 3/4)\n",
    "5 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-ensemble-classification]\n",
    "Succeeded\n",
    "0.90911\n",
    "0.91763\n",
    "sever-ensemble-3(version 4/11)\n",
    "5 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-ensemble-3]\n",
    "Succeeded\n",
    "0.90515\n",
    "0.91738\n",
    "sever-ensemble-classification(version 2/4)\n",
    "5 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-ensemble-classification]\n",
    "Succeeded\n",
    "0.90951\n",
    "0.91832\n",
    "sever-ensemble-classification(version 1/4)\n",
    "6 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-ensemble-classification]\n",
    "Succeeded\n",
    "0.91023\n",
    "0.91817\n",
    "sever-ensemble-3(version 2/11)\n",
    "6 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-ensemble-3]\n",
    "Succeeded\n",
    "0.90612\n",
    "0.91817\n",
    "sever-submission-f32(version 40/50)\n",
    "6 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-submission-f32]\n",
    "Succeeded\n",
    "0.86824\n",
    "0.90397\n",
    "sever-submission-f32(version 39/50)\n",
    "6 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-submission-f32]\n",
    "Succeeded\n",
    "0.89833\n",
    "0.91656\n",
    "sever-ensemble-3(version 1/11)\n",
    "7 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-ensemble-3]\n",
    "Succeeded\n",
    "0.90279\n",
    "0.91763\n",
    "sever-submission-f32(version 37/50)\n",
    "7 days ago by Karl Hornlund\n",
    "\n",
    "From Kernel [sever-submission-f32]\n",
    "Succeeded\n",
    "0.88942\n",
    "0.91489\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>private</th>\n",
       "      <th>public</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.90370</td>\n",
       "      <td>0.91840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.90274</td>\n",
       "      <td>0.91844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.89713</td>\n",
       "      <td>0.91829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.88639</td>\n",
       "      <td>0.91794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.90259</td>\n",
       "      <td>0.91854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   private   public\n",
       "0  0.90370  0.91840\n",
       "1  0.90274  0.91844\n",
       "2  0.89713  0.91829\n",
       "3  0.88639  0.91794\n",
       "4  0.90259  0.91854"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submissions = []\n",
    "submission = {}\n",
    "counter = 0\n",
    "lines_per_chunk = 7\n",
    "for idx, line in enumerate(s.split('\\n')):\n",
    "    if idx % lines_per_chunk == 6:\n",
    "        submission['private'] = float(line)\n",
    "    if idx % lines_per_chunk == 0 and idx != 0:\n",
    "        submission['public'] = float(line)\n",
    "        submissions.append(submission)\n",
    "        submission = {}\n",
    "df = pd.DataFrame(submissions)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = list(reversed(range(df.shape[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[df['public'] > 0.91, :]\n",
    "df = df.loc[df['private'] > 0.90, :]\n",
    "df['winner'] = 0.90883"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>private</th>\n",
       "      <th>public</th>\n",
       "      <th>winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.90370</td>\n",
       "      <td>0.91840</td>\n",
       "      <td>0.90883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.90274</td>\n",
       "      <td>0.91844</td>\n",
       "      <td>0.90883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.90259</td>\n",
       "      <td>0.91854</td>\n",
       "      <td>0.90883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.90876</td>\n",
       "      <td>0.91809</td>\n",
       "      <td>0.90883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.90951</td>\n",
       "      <td>0.91812</td>\n",
       "      <td>0.90883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    private   public   winner\n",
       "31  0.90370  0.91840  0.90883\n",
       "30  0.90274  0.91844  0.90883\n",
       "27  0.90259  0.91854  0.90883\n",
       "26  0.90876  0.91809  0.90883\n",
       "23  0.90951  0.91812  0.90883"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "?ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.cla()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
